 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q03001 from www.uniprot.org...
The NucPred score for your sequence is 0.98 (see score help below)
1 MAGYLSPAAYLYVEEQEYLQAYEDVLERYKDERDKVQKKTFTKWINQHLM 50
51 KVRKHVNDLYEDLRDGHNLISLLEVLSGDTLPREKGRMRFHRLQNVQIAL 100
101 DYLKRRQVKLVNIRNDDITDGNPKLTLGLIWTIILHFQISDIHVTGESED 150
151 MSAKERLLLWTQQATEGYAGIRCENFTTCWRDGKLFNAIIHKYRPDLIDM 200
201 NTVAVQSNLANLEHAFYVAEKIGVIRLLDPEDVDVSSPDEKSVITYVSSL 250
251 YDAFPKVPEGGEGIGANDVEVKWIEYQNMVNYLIQWIRHHVTTMSERTFP 300
301 NNPVELKALYNQYLQFKETEIPPKETEKSKIKRLYKLLEIWIEFGRIKLL 350
351 QGYHPNDIEKEWGKLIIAMLEREKALRPEVERLEMLQQIANRVQRDSVIC 400
401 EDKLILAGNALQSDSKRLESGVQFQNEAEIAGYILECENLLRQHVIDVQI 450
451 LIDGKYYQADQLVQRVAKLRDEIMALRNECSSVYSKGRILTTEQTKLMIS 500
501 GITQSLNSGFAQTLHPSLTSGLTQSLTPSLTSSSMTSGLSSGMTSRLTPS 550
551 VTPAYTPGFPSGLVPNFSSGVEPNSLQTLKLMQIRKPLLKSSLLDQNLTE 600
601 EEINMKFVQDLLNWVDEMQVQLDRTEWGSDLPSVESHLENHKNVHRAIEE 650
651 FESSLKEAKISEIQMTAPLKLTYAEKLHRLESQYAKLLNTSRNQERHLDT 700
701 LHNFVSRATNELIWLNEKEEEEVAYDWSERNTNIARKKDYHAELMRELDQ 750
751 KEENIKSVQEIAEQLLLENHPARLTIEAYRAAMQTQWSWILQLCQCVEQH 800
801 IKENTAYFEFFNDAKEATDYLRNLKDAIQRKYSCDRSSSIHKLEDLVQES 850
851 MEEKEELLQYKSTIANLMGKAKTIIQLKPRNSDCPLKTSIPIKAICDYRQ 900
901 IEITIYKDDECVLANNSHRAKWKVISPTGNEAMVPSVCFTVPPPNKEAVD 950
951 LANRIEQQYQNVLTLWHESHINMKSVVSWHYLINEIDRIRASNVASIKTM 1000
1001 LPGEHQQVLSNLQSRFEDFLEDSQESQVFSGSDITQLEKEVNVCKQYYQE 1050
1051 LLKSAEREEQEESVYNLYISEVRNIRLRLENCEDRLIRQIRTPLERDDLH 1100
1101 ESVFRITEQEKLKKELERLKDDLGTITNKCEEFFSQAAASSSVPTLRSEL 1150
1151 NVVLQNMNQVYSMSSTYIDKLKTVNLVLKNTQAAEALVKLYETKLCEEEA 1200
1201 VIADKNNIENLISTLKQWRSEVDEKRQVFHALEDELQKAKAISDEMFKTY 1250
1251 KERDLDFDWHKEKADQLVERWQNVHVQIDNRLRDLEGIGKSLKYYRDTYH 1300
1301 PLDDWIQQVETTQRKIQENQPENSKTLATQLNQQKMLVSEIEMKQSKMDE 1350
1351 CQKYAEQYSATVKDYELQTMTYRAMVDSQQKSPVKRRRMQSSADLIIQEF 1400
1401 MDLRTRYTALVTLMTQYIKFAGDSLKRLEEEEKSLEEEKKEHVEKAKELQ 1450
1451 KWVSNISKTLKDAEKAGKPPFSKQKISSEEISTKKEQLSEALQTIQLFLA 1500
1501 KHGDKMTDEERNELEKQVKTLQESYNLLFSESLKQLQESQTSGDVKVEEK 1550
1551 LDKVIAGTIDQTTGEVLSVFQAVLRGLIDYDTGIRLLETQLMISGLISPE 1600
1601 LRKCFDLKDAKSHGLIDEQILCQLKELSKAKEIISAASPTTIPVLDALAQ 1650
1651 SMITESMAIKVLEILLSTGSLVIPATGEQLTLQKAFQQNLVSSALFSKVL 1700
1701 ERQNMCKDLIDPCTSEKVSLIDMVQRSTLQENTGMWLLPVRPQEGGRITL 1750
1751 KCGRNISILRAAHEGLIDRETMFRLLSAQLLSGGLINSNSGQRMTVEEAV 1800
1801 REGVIDRDTASSILTYQVQTGGIIQSNPAKRLTVDEAVQCDLITSSSALL 1850
1851 VLEAQRGYVGLIWPHSGEIFPTSSSLQQELITNELAYKILNGRQKIAALY 1900
1901 IPESSQVIGLDAAKQLGIIDNNTASILKNITLPDKMPDLGDLEACKNARR 1950
1951 WLSFCKFQPSTVHDYRQEEDVFDGEEPVTTQTSEETKKLFLSYLMINSYM 2000
2001 DANTGQRLLLYDGDLDEAVGMLLEGCHAEFDGNTAIKECLDVLSSSGVFL 2050
2051 NNASGREKDECTATPSSFNKCHCGEPEHEETPENRKCAIDEEFNEMRNTV 2100
2101 INSEFSQSGKLASTISIDPKVNSSPSVCVPSLISYLTQTELADISMLRSD 2150
2151 SENILTNYENQSRVETNERANECSHSKNIQNFPSDLIENPIMKSKMSKFC 2200
2201 GVNETENEDNTNRDSPIFDYSPRLSALLSHDKLMHSQGSFNDTHTPESNG 2250
2251 NKCEAPALSFSDKTMLSGQRIGEKFQDQFLGIAAINISLPGEQYGQKSLN 2300
2301 MISSNPQVQYHNDKYISNTSGEDEKTHPGFQQMPEDKEDESEIEEYSCAV 2350
2351 TPGGDTDNAIVSLTCATPLLDETISASDYETSLLNDQQNNTGTDTDSDDD 2400
2401 FYDTPLFEDDDHDSLLLDGDDRDCLHPEDYDTLQEENDETASPADVFYDV 2450
2451 SKENENSMVPQGAPVGSLSVKNKAHCLQDFLMDVEKDELDSGEKIHLNPV 2500
2501 GSDKVNGQSLETGSERECTNILEGDESDSLTDYDIVGGKESFTASLKFDD 2550
2551 SGSWRGRKEEYVTGQEFHSDTDHLDSMQSEESYGDYIYDSNDQDDDDDDG 2600
2601 IDEEGGGIRDENGKPRCQNVAEDMDIQLCASILNENSDENENINTMILLD 2650
2651 KMHSCSSLEKQQRVNVVQLASPSENNLVTEKSNLPEYTTEIAGKSKENLL 2700
2701 NHEMVLKDVLPPIIKDTESEKTFGPASISHDNNNISSTSELGTDLANTKV 2750
2751 KLIQGSELPELTDSVKGKDEYFKNMTPKVDSSLDHIICTEPDLIGKPAEE 2800
2801 SHLSLIASVTDKDPQGNGSDLIKGRDGKSDILIEDETSIQKMYLGEGEVL 2850
2851 VEGLVEEENRHLKLLPGKNTRDSFKLINSQFPFPQITNNEELNQKGSLKK 2900
2901 ATVTLKDEPNNLQIIVSKSPVQFENLEEIFDTSVSKEISDDITSDITSWE 2950
2951 GNTHFEESFTDGPEKELDLFTYLKHCAKNIKAKDVAKPNEDVPSHVLITA 3000
3001 PPMKEHLQLGVNNTKEKSTSTQKDSPLNDMIQSNDLCSKESISGGGTEIS 3050
3051 QFTPESIEATLSILSRKHVEDVGKNDFLQSERCANGLGNDNSSNTLNTDY 3100
3101 SFLEINNKKERIEQQLPKEQALSPRSQEKEVQIPELSQVFVEDVKDILKS 3150
3151 RLKEGHMNPQEVEEPSACADTKILIQNLIKRITTSQLVNEASTVPSDSQM 3200
3201 SDSSGVSPMTNSSELKPESRDDPFCIGNLKSELLLNILKQDQHSQKITGV 3250
3251 FELMRELTHMEYDLEKRGITSKVLPLQLENIFYKLLADGYSEKIEHVGDF 3300
3301 NQKACSTSEMMEEKPHILGDIKSKEGNYYSPNLETVKEIGLESSTVWAST 3350
3351 LPRDEKLKDLCNDFPSHLECTSGSKEMASGDSSTEQFSSELQQCLQHTEK 3400
3401 MHEYLTLLQDMKPPLDNQESLDNNLEALKNQLRQLETFELGLAPIAVILR 3450
3451 KDMKLAEEFLKSLPSDFPRGHVEELSISHQSLKTAFSSLSNVSSERTKQI 3500
3501 MLAIDSEMSKLAVSHEEFLHKLKSFSDWVSEKSKSVKDIEIVNVQDSEYV 3550
3551 KKRLEFLKNVLKDLGHTKMQLETTAFDVQFFISEYAQDLSPNQSKQLLRL 3600
3601 LNTTQKCFLDVQESVTTQVERLETQLHLEQDLDDQKIVAERQQEYKEKLQ 3650
3651 GICDLLTQTENRLIGHQEAFMIGDGTVELKKYQSKQEELQKDMQGSAQAL 3700
3701 AEVVKNTENFLKENGEKLSQEDKALIEQKLNEAKIKCEQLNLKAEQSKKE 3750
3751 LDKVVTTAIKEETEKVAAVKQLEESKTKIENLLDWLSNVDKDSERAGTKH 3800
3801 KQVIEQNGTHFQEGDGKSAIGEEDEVNGNLLETDVDGQVGTTQENLNQQY 3850
3851 QKVKAQHEKIISQHQAVIIATQSAQVLLEKQGQYLSPEEKEKLQKNMKEL 3900
3901 KVHYETALAESEKKMKLTHSLQEELEKFDADYTEFEHWLQQSEQELENLE 3950
3951 AGADDINGLMTKLKRQKSFSEDVISHKGDLRYITISGNRVLEAAKSCSKR 4000
4001 DGGKVDTSATHREVQRKLDHATDRFRSLYSKCNVLGNNLKDLVDKYQHYE 4050
4051 DASCGLLAGLQACEATASKHLSEPIAVDPKNLQRQLEETKALQGQISSQQ 4100
4101 VAVEKLKKTAEVLLDARGSLLPAKNDIQKTLDDIVGRYEDLSKSVNERNE 4150
4151 KLQITLTRSLSVQDGLDEMLDWMGNVESSLKEQGQVPLNSTALQDIISKN 4200
4201 IMLEQDIAGRQSSINAMNEKVKKFMETTDPSTASSLQAKMKDLSARFSEA 4250
4251 SHKHKETLAKMEELKTKVELFENLSEKLQTFLETKTQALTEVDVPGKDVT 4300
4301 ELSQYMQESTSEFLEHKKHLEVLHSLLKEISSHGLPSDKALVLEKTNNLS 4350
4351 KKFKEMEDTIKEKKEAVTSCQEQLDAFQVLVKSLKSWIKETTKKVPIVQP 4400
4401 SFGAEDLGKSLEDTKKLQEKWSLKTPEIQKVNNSGISLCNLISAVTTPAK 4450
4451 AIAAVKSGGAVLNGEGTATNTEEFWANKGLTSIKKDMTDISHGYEDLGLL 4500
4501 LKDKIAELNTKLSKLQKAQEESSAMMQWLQKMNKTATKWQQTPAPTDTEA 4550
4551 VKTQVEQNKSFEAELKQNVNKVQELKDKLTELLEENPDTPEAPRWKQMLT 4600
4601 EIDSKWQELNQLTIDRQQKLEESSNNLTQFQTVEAQLKQWLVEKELMVSV 4650
4651 LGPLSIDPNMLNTQRQQVQILLQEFATRKPQYEQLTAAGQGILSRPGEDP 4700
4701 SLRGIVKEQLAAVTQKWDSLTGQLSDRCDWIDQAIVKSTQYQSLLRSLSD 4750
4751 KLSDLDNKLSSSLAVSTHPDAMNQQLETAQKMKQEIQQEKKQIKVAQALC 4800
4801 EDLSALVKEEYLKAELSRQLEGILKSFKDVEQKAENHVQHLQSACASSHQ 4850
4851 FQQMSRDFQAWLDTKKEEQNKSHPISAKLDVLESLIKDHKDFSKTLTAQS 4900
4901 HMYEKTIAEGENLLLKTQGSEKAALQLQLNTIKTNWDTFNKQVKERENKL 4950
4951 KESLEKALKYKEQVETLWPWIDKCQNNLEEIKFCLDPAEGENSIAKLKSL 5000
5001 QKEMDQHFGMVELLNNTANSLLSVCEIDKEVVTDENKSLIQKVDMVTEQL 5050
5051 HSKKFCLENMTQKFKEFQEVSKESKRQLQCAKEQLDIHDSLGSQAYSNKY 5100
5101 LTMLQTQQKSLQALKHQVDLAKRLAQDLVVEASDSKGTSDVLLQVETIAQ 5150
5151 EHSTLSQQVDEKCSFLETKLQGIGHFQNTIREMFSQFAEFDDELDSMAPV 5200
5201 GRDAETLQKQKETIKAFLKKLEALMASNDNANKTCKMMLATEETSPDLVG 5250
5251 IKRDLEALSKQCNKLLDRAQAREEQVEGTIKRLEEFYSKLKEFSILLQKA 5300
5301 EEHEESQGPVGMETETINQQLNMFKVFQKEEIEPLQGKQQDVNWLGQGLI 5350
5351 QSAAKSTSTQGLEHDLDDVNARWKTLNKKVAQRAAQLQEALLHCGRFQDA 5400
5401 LESLLSWMVDTEELVANQKPPSAEFKVVKAQIQEQKLLQRLLDDRKSTVE 5450
5451 VIKREGEKIATTAEPADKVKILKQLSLLDSRWEALLNKAETRNRQLEGIS 5500
5501 VVAQQFHETLEPLNEWLTTIEKRLVNCEPIGTQASKLEEQIAQHKALEDD 5550
5551 IINHNKHLHQAVSIGQSLKVLSSREDKDMVQSKLDFSQVWYIEIQEKSHS 5600
5601 RSELLQQALCNAKIFGEDEVELMNWLNEVHDKLSKLSVQDYSTEGLWKQQ 5650
5651 SELRVLQEDILLRKQNVDQALLNGLELLKQTTGDEVLIIQDKLEAIKARY 5700
5701 KDITKLSTDVAKTLEQALQLARRLHSTHEELCTWLDKVEVELLSYETQVL 5750
5751 KGEEASQAQMRPKELKKEAKNNKALLDSLNEVSSALLELVPWRAREGLEK 5800
5801 MVAEDNERYRLVSDTITQKVEEIDAAILRSQQFDQAADAELSWITETEKK 5850
5851 LMSLGDIRLEQDQTSAQLQVQKTFTMEILRHKDIIDDLVKSGHKIMTACS 5900
5901 EEEKQSMKKKLDKVLKNYDTICQINSERYLQLERAQSLVNQFWETYEELW 5950
5951 PWLTETQSIISQLPAPALEYETLRQQQEEHRQLRELIAEHKPHIDKMNKT 6000
6001 GPQLLELSPGEGFSIQEKYVAADTLYSQIKEDVKKRAVALDEAISQSTQF 6050
6051 HDKIDQILESLERIVERLRQPPSISAEVEKIKEQISENKNVSVDMEKLQP 6100
6101 LYETLKQRGEEMIARSGGTDKDISAKAVQDKLDQMVFIWENIHTLVEERE 6150
6151 AKLLDVMELAEKFWCDHMSLIVTIKDTQDFIRDLEDPGIDPSVVKQQQEA 6200
6201 AETIREEIDGLQEELDIVINLGSELIAACGEPDKPIVKKSIDELNSAWDS 6250
6251 LNKAWKDRIDKLEEAMQAAVQYQDGLQAVFDWVDIAGGKLASMSPIGTDL 6300
6301 ETVKQQIEELKQFKSEAYQQQIEMERLNHQAELLLKKVTEESDKHTVQDP 6350
6351 LMELKLIWDSLEERIINRQHKLEGALLALGQFQHALDELLAWLTHTEGLL 6400
6401 SEQKPVGGDPKAIEIELAKHHVLQNDVLAHQSTVEAVNKAGNDLIESSAG 6450
6451 EEASNLQNKLEVLNQRWQNVLEKTEQRKQQLDGALRQAKGFHGEIEDLQQ 6500
6501 WLTDTERHLLASKPLGGLPETAKEQLNVHMEVCAAFEAKEETYKSLMQKG 6550
6551 QQMLARCPKSAETNIDQDINNLKEKWESVETKLNERKTKLEEALNLAMEF 6600
6601 HNSLQDFINWLTQAEQTLNVASRPSLILDTVLFQIDEHKVFANEVNSHRE 6650
6651 QIIELDKTGTHLKYFSQKQDVVLIKNLLISVQSRWEKVVQRLVERGRSLD 6700
6701 DARKRAKQFHEAWSKLMEWLEESEKSLDSELEIANDPDKIKTQLAQHKEF 6750
6751 QKSLGAKHSVYDTTNRTGRSLKEKTSLADDNLKLDDMLSELRDKWDTICG 6800
6801 KSVERQNKLEEALLFSGQFTDALQALIDWLYRVEPQLAEDQPVHGDIDLV 6850
6851 MNLIDNHKAFQKELGKRTSSVQALKRSARELIEGSRDDSSWVKVQMQELS 6900
6901 TRWETVCALSISKQTRLEAALRQAEEFHSVVHALLEWLAEAEQTLRFHGV 6950
6951 LPDDEDALRTLIDQHKEFMKKLEEKRAELNKATTMGDTVLAICHPDSITT 7000
7001 IKHWITIIRARFEEVLAWAKQHQQRLASALAGLIAKQELLEALLAWLQWA 7050
7051 ETTLTDKDKEVIPQEIEEVKALIAEHQTFMEEMTRKQPDVDKVTKTYKRR 7100
7101 AADPSSLQSHIPVLDKGRAGRKRFPASSLYPSGSQTQIETKNPRVNLLVS 7150
7151 KWQQVWLLALERRRKLNDALDRLEELREFANFDFDIWRKKYMRWMNHKKS 7200
7201 RVMDFFRRIDKDQDGKITRQEFIDGILSSKFPTSRLEMSAVADIFDRDGD 7250
7251 GYIDYYEFVAALHPNKDAYKPITDADKIEDEVTRQVAKCKCAKRFQVEQI 7300
7301 GDNKYRFFLGNQFGDSQQLRLVRILRSTVMVRVGGGWMALDEFLVKNDPC 7350
7351 RVHHHGSKMLRSESNSSITTTQPTIAKGRTNMELREKFILADGASQGMAA 7400
7401 FRPRGRRSRPSSRGASPNRSTSVSSQAAQAASPQVPATTTPKGTPIQGSK 7450
7451 LRLPGYLSGKGFHSGEDSGLITTAAARVRTQFADSKKTPSRPGSRAGSKA 7500
7501 GSRASSRRGSDASDFDISEIQSVCSDVETVPQTHRPTPRAGSRPSTAKPS 7550
7551 KIPTPQRKSPASKLDKSSKR 7570
Positively and negatively influencing subsequences are coloured according to the following scale:
(non-nuclear) negative ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| positive (nuclear)
What does the NucPred score mean?
| You have to decide on a NucPred score threshold. Sequences which score greater than or equal to this threshold are predicted to spend some time in the nucleus. Higher thresholds yield fewer predicted nuclear proteins, but these predictions are more accurate (you can have higher confidence in them). The table below gives more details of the performance of NucPred estimated using the sequences it was trained on (by cross-validation). Another benchmark is available in the Bioinformatics 2007 paper. |
| NucPred score threshold | Specificity | Sensitivity |
| see above | fraction of proteins predicted to be nuclear that actually are nuclear | fraction of true nuclear proteins that are predicted (coverage) |
| 0.10 | 0.45 | 0.88 |
| 0.20 | 0.52 | 0.83 |
| 0.30 | 0.57 | 0.77 |
| 0.40 | 0.63 | 0.69 |
| 0.50 | 0.70 | 0.62 |
| 0.60 | 0.71 | 0.53 |
| 0.70 | 0.81 | 0.44 |
| 0.80 | 0.84 | 0.32 |
| 0.90 | 0.88 | 0.21 |
| 1.00 | 1.00 | 0.02 |
| Sequences which score >= 0.8 with NucPred and which
are predicted by PredictNLS to contain an NLS have been shown to be 93% correct with a coverage of 16%. (PredictNLS by itself is 87% correct with 26% coverage on the same data.) |
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